Much of the buzz in artificial intelligence today is about generative AI and the way AI is being used to accelerate software and products for consumers. Today, an AI startup called PhysicsX, co-founded by two theoretical physicists – including a Formula 1 engineering superstar – is emerging from stealth with a very specific focus on building and operating physical systems in the business world.
London-based PhysicsX has devised an AI platform to create and run simulations for engineers working on projects in automotive, aerospace and materials science manufacturing – industries where there are frequent bottlenecks in manufacturing development because of the way models are tested before production. It’s coming out of stealth today with $32 million in funding.
The round, a Series A, is led by General Catalyst. Others in the round include a very interesting mix of financial and strategic backers. They include Standard Industries, NGP Energy, Radius Capital and KKR co-founder and co-executive chairman Henry Kravis. The funding will be used for business development and to further develop the business platform. This is PhysicsX’s first external financing.
PhysicX addresses a problem that has been very consistent, yet overlooked in the world of manufacturing and physical production.
In any physical system, whether in an experimental laboratory or a live industrial environment, whenever a new idea is introduced – for example, a theory on improving the operational efficiency of a machine, not to mention working on completely new products – engineers will need to simulate how the new idea will work before committing to developing it, and further improving the way it works. Typically, that simulation and testing work is done by scientists, engineers who may use some AI in the process, but ultimately work out the process manually.
“Something like airflow over an object can take an hour or two hours, but if you want to simulate something more complex it can take a day or more. There are therefore calculation costs and therefore also time costs involved. And that limits the depth at which you can optimize,” says Robin Tuluie, who co-founded PhysicsX with Jacomo Corbo, in an interview.
The pair knows the pain points very well firsthand.
Tuluie has already had two different lives as a theoretical physicist. As an academic he worked with Nobel Prize winners with a focus on astrophysics. He then moved into racing, first at Renault and then Mercedes, as Head of R&D and Chief Scientist respectively, where he came up with designs that helped his teams win four Formula 1 World Championships (gaining some fame himself in the process). He also worked for years at Bentley and Volkswagen on car design.
Corbo, who has a PhD from Harvard, has also worked in racing, but most recently he founded and led QuantumBlack, the AI labs at McKinsey, and worked with a number of Formula 1 and other automotive and industrial clients on thorny product engineering problems . .
The pair have assembled a team of no fewer than 50 scientists – other mechanical engineering specialists, physicists and more – to build out the PhysicsX platform, which focuses on the automotive sector, but also a much wider range of applications , said Corbo.
“We are building an enterprise platform to support a fairly broad range of domain applications related to construction and optimization problems and bottlenecks in physics simulation,” he said. “What PhysicsX buys you is the ability to predict physics [of a system] with very, very high accuracy and reliability, and does it anywhere from 10,000 to a million times faster. Now be a lot more advanced when it comes to things like mining, in a very high-dimensional space.”
The rise of PhysicsX comes at a very timely time in the world of deep learning and AI, especially in how it is applied to the physical world.
Just earlier this month, DeepMind released new research on how it applied highly advanced machine learning to the world of short- and long-term weather forecasting, and Corbo believes that physical twist will underline the next frontier of AI research and development.
“This is the first time that AI models, these deep learning models, these geometric deep learning models, are overtaking numerical simulation for weather,” Corbo points out. “We’re starting to see that happen in physics more broadly. And that makes many different applications in the field of engineering possible. That’s why we’re building a platform to do that across all sectors and across a wide range of domain problems.”
More generally, companies have faced many challenges when it comes to digital transformation: they have stripped away existing infrastructure to adopt more modern IT and approaches. While you could also classify what PhysicsX is doing as a kind of “digital transformation,” the startup is able to sidestep these challenges because the types of applications it tackles, in the areas of engineering and R&D, are not typically IT problems that require scaling. organizations more broadly.
Yet it is a new approach that will disrupt the way industrial companies approach development today. General Catalyst is therefore betting on a very hot area – AI – but also breaking new ground by backing a startup that believes how that hot area will evolve.
“PhysicsX is pushing the technical boundaries in critical industries, led by a team highly skilled in simulation engineering and machine learning,” said Larry Bohn, MD of General Catalyst, in a statement. “With credibility, customer relationships and technical expertise, we believe PhysicsX is poised to transform engineering in complex industries. This aligns with our vision for industrial transformation and positions PhysicsX with the opportunity to create a category-defining company in advanced industries.”